iwla (Intelligent Web Log Analyzer) is basically a clone of [awstats](http://www.awstats.org). The main problem with awstats is that it's a very monolothic project with everything in one big PERL file. In opposite, iwla has be though to be very modular : a small core analysis and a lot of filters. It can be viewed as UNIX pipes. Philosophy of iwla is : add, update, delete ! That's the job of each filters : modify statistics until final result. It's written in Python.
Nevertheless, iwla is only focused on HTTP logs. It uses data (robots definitions, search engines definitions) and design from awstats. Moreover, it's not dynamic, but only generates static HTML page (with gzip compression option).
-c : Clean output (database and HTML) before starting
-i : Read data from stdin instead of conf.analyzed_filename
-f : Read data from FILE instead of conf.analyzed_filename
-d : Loglevel in ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL']
Basic usage
-----------
In addition to command line, iwla read parameters in default_conf.py. User can override default values using _conf.py_ file. Each module requires its own parameters.
Main values to edit are :
* **analyzed_filename** : web server log
* **domaine_name** : domain name to filter
* **pre_analysis_hooks** : List of pre analysis hooks
* **post_analysis_hooks** : List of post analysis hooks
* **display_hooks** : List of display hooks
* **locale** : Displayed locale (_en_ or _fr_)
Then, you can then iwla. Output HTML files are created in _output_ directory by default. To quickly see it go in output and type
python -m SimpleHTTPServer 8000
Open your favorite web browser at _http://localhost:8000_. Enjoy !
**Warning** : The order is hooks list is important : Some plugins may requires others plugins, and the order of display_hooks is the order of displayed blocks in final result.
* **DISPLAY_ROOT** : Default HTML output directory (default ./output)
* **log_format** : Web server log format (nginx style). Default is apache log format
* **time_format** : Time format used in log format
* **pages_extensions** : Extensions that are considered as a HTML page (or result) in opposit to hits
* **viewed_http_codes** : HTTP codes that are cosidered OK (200, 304)
* **count_hit_only_visitors** : If False, doesn't cout visitors that doesn't GET a page but resources only (images, rss...)
* **multimedia_files** : Multimedia extensions (not accounted as downloaded files)
* **css_path** : CSS path (you can add yours)
* **compress_output_files** : Files extensions to compress in gzip during display build
Plugins
-------
As previously described, plugins acts like UNIX pipes : statistics are constantly updated by each plugin to produce final result. We have three type of plugins :
* **Pre analysis plugins** : Called before generating days statistics. They are in charge to filter robots, crawlers, bad pages...
* **Post analysis plugins** : Called after basic statistics computation. They are in charge to enlight them with their own algorithms
* **Display plugins** : They are in charge to produce HTML files from statistics.
To use plugins, just insert their name in _pre_analysis_hooks_, _post_analysis_hooks_ and _display_hooks_ lists in conf.py.
Statistics are stored in dictionaries :
* **month_stats** : Statistics of current analysed month
* **valid_visitor** : A subset of month_stats without robots
* **days_stats** : Statistics of current analysed day
* **visits** : All visitors with all of its requests
* **meta** : Final result of month statistics (by year)
Create a Plugins
----------------
To create a new plugin, it's necessary to create a derived class of IPlugin (_iplugin.py) in the right directory (_plugins/xxx/yourPlugin.py_).
Plugins can defines required configuration values (self.conf_requires) that must be set in conf.py (or can be optional). They can also defines required plugins (self.requires).
The two functions to overload are _load(self)_ that must returns True or False if all is good (or not). It's called after _init_. The second is _hook(self)_ that is the body of plugins.
For display plugins, a lot of code has been wrote in _display.py_ that simplify the creation on HTML blocks, tables and bar graphs.
Plugins
=======
Optional configuration values ends with *.
iwla
----
Main class IWLA
Parse Log, compute them, call plugins and produce output
For now, only HTTP log are valid
Plugin requirements :
None
Conf values needed :
analyzed_filename
domain_name
locales_path
compress_output_files*
Output files :
DB_ROOT/meta.db
DB_ROOT/year/month/iwla.db
OUTPUT_ROOT/index.html
OUTPUT_ROOT/year/month/index.html
Statistics creation :
meta :
last_time
start_analysis_time
stats =>
year =>
month =>
viewed_bandwidth
not_viewed_bandwidth
viewed_pages
viewed_hits
nb_visits
nb_visitors
month_stats :
viewed_bandwidth
not_viewed_bandwidth
viewed_pages
viewed_hits
nb_visits
days_stats :
day =>
viewed_bandwidth
not_viewed_bandwidth
viewed_pages
viewed_hits
nb_visits
nb_visitors
visits :
remote_addr =>
remote_addr
remote_ip
viewed_pages
viewed_hits
not_viewed_pages
not_viewed_hits
bandwidth
last_access
requests =>
[fields_from_format_log]
extract_request =>
extract_uri
extract_parameters*
extract_referer* =>
extract_uri
extract_parameters*
robot
hit_only
is_page
valid_visitors:
month_stats without robot and hit only visitors (if not conf.count_hit_only_visitors)
